A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

Wong, Ka-Chun; Li, Yue; Peng, Chengbin; Wong, Hau-San

doi:10.1109/tcbb.2015.2443782

Cited by 17 publications

(8 citation statements)

References 49 publications

(63 reference statements)

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“…More recently, high-throughput approaches to identify protein-DNA interactions have been developed; these techniques include Protein-Binding Microarray (PBM), Cyclical Amplification and Selection of Targets (CAST), Systematic Evolution of Ligands by Exponential Enrichment (SELEX), Serial Analysis of Gene Expression (SAGE) and Bind-n-seq [12–16]. In PBM, proteins bind double-stranded oligonucleotides on a microarray [13]. CAST generally involves several rounds of amplification and purification for each protein and is therefore labour-intensive [14, 15].…”

Section: Introductionmentioning

confidence: 99%

An improved bind-n-seq strategy to determine protein-DNA interactions validated using the bacterial transcriptional regulator YipR

Valvano

et al. 2020

BMC Microbiol

134

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BackgroundInteractions between transcription factors and DNA lie at the centre of many biological processes including DNA recombination, replication, repair and transcription. Most bacteria encode diverse proteins that act as transcription factors to regulate various traits. Several technologies for identifying protein–DNA interactions at the genomic level have been developed. Bind-n-seq is a high-throughput in vitro method first deployed to analyse DNA interactions associated with eukaryotic zinc-finger proteins. The method has three steps (i) binding protein to a randomised oligonucleotide DNA target library, (ii) deep sequencing of bound oligonucleotides, and (iii) a computational algorithm to define motifs among the sequences. The classical Bind-n-seq strategy suffers from several limitations including a lengthy wet laboratory protocol and a computational algorithm that is difficult to use. We introduce here an improved, rapid, and simplified Bind-n-seq protocol coupled with a user-friendly downstream data analysis and handling algorithm, which has been optimized for bacterial target proteins. We validate this new protocol by showing the successful characterisation of the DNA-binding specificities of YipR (YajQ interacting protein regulator), a well-known transcriptional regulator of virulence genes in the bacterial phytopathogen Xanthomonas campestris pv. campestris (Xcc).ResultsThe improved Bind-n-seq approach identified several DNA binding motif sequences for YipR, in particular the CCCTCTC motif, which were located in the promoter regions of 1320 Xcc genes. Informatics analysis revealed that many of these genes regulate functions associated with virulence, motility, and biofilm formation and included genes previously found involved in virulence. Additionally, electromobility shift assays show that YipR binds to the promoter region of XC_2633 in a CCCTCTC motif-dependent manner.ConclusionWe present a new and rapid Bind-n-seq protocol that should be useful to investigate DNA-binding proteins in bacteria. The analysis of YipR DNA binding using this protocol identifies a novel DNA sequence motif in the promoter regions of target genes that define the YipR regulon.

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Section: Introductionmentioning

confidence: 99%

An improved bind-n-seq strategy to determine protein-DNA interactions validated using the bacterial transcriptional regulator YipR

Valvano

et al. 2020

BMC Microbiol

134

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“…These interactions perform several biological functions in the cells of the living organisms [1]. The DNA-protein interactions are the fundamental type of interactions for almost all biological activities and processes, such as transcription, gene expression regulation, repair, packaging of chromosomal DNA, and splicing [2][3][4][5]. Several experimental methods are used to confirm the interactions between protein and DNA-binding residues.…”

Section: Introductionmentioning

confidence: 99%

DBpred: A deep learning method for the prediction of DNA interacting residues in protein sequences

Patiyal

Dhall

Raghava

2021

Preprint

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DNA-protein interaction is one of the most crucial interactions in the biological system, which decide the fate of many processes such as transcription, regulation of gene expression, splicing, and many more. Though many computational approaches exist that can predict the DNA interacting residues from the protein sequences, there is still a significant opportunity for improvement in terms of performance and accessibility. In this study, we have downloaded the benchmark dataset from method hybridNAP and recently published method ProNA2020, for training and validation purposes, that comprise 864 and 308 proteins, respectively. We have implemented CD-HIT software to handle the redundancy with 30% identity, and left with 646 proteins for training and 46 proteins for validation purposes, in which the validation dataset do not share more than 30% of sequence identity with the training dataset. We have generated amino acid binary profiles, physicochemical-properties based binary profiles, PSSM profiles, and a combination of all profiles described as hybrid feature. 1D-CNN based model performed best as compared to other models for each set of features. The model developed using amino acid binary profile achieved AUROC of 0.83 and 0.74 for training and validation dataset. Using physicochemical properties based binary profile, model attained AUROC of 0.86 and 0.73 for training and validation dataset. Model generated using PSSM profile resulted in the better performance with AUROC 0.91 and 0.74 for training and validation dataset. And, model developed using hybrid of all features performed best with AUROC of 0.91, and 0.79 for training and validation dataset, respectively. We have compared our method’s performance with the current approach and shown improvements. We have included the best-performing models in the standalone and web server accessible at https://webs.iiitd.edu.in/raghava/dbpred. DBPred is an effective approach to predict the DNA interacting residues in the protein using its primary structure.

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“…In biochemistry, the protein-DNA interaction is considered to be one of the vital activities that has strong impacts on diverse biological events including DNA synthesis, transcription, splicing, and restoration [1][2][3]. Therefore, high precision in determining the protein-DNA binding residues is crucial not only for protein function analysis but also for novel drug discovery [4].…”

Section: Introductionmentioning

confidence: 99%

IProDNA-CapsNet: Identifying protein-DNA binding residues using capsule neural networks

Nguyen¹,

Nguyen²,

Doan-Ngoc³

et al. 2021

Preprint

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© 2019 The Author(s). Background: Since protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in the age of post-genomic where data on protein sequences have expanded very fast. In this study, we propose iProDNA-CapsNet - a new prediction model identifying protein-DNA binding residues using an ensemble of capsule neural networks (CapsNets) on position specific scoring matrix (PSMM) profiles. The use of CapsNets promises an innovative approach to determine the location of DNA-binding residues. In this study, the benchmark datasets introduced by Hu et al. (2017), i.e., PDNA-543 and PDNA-TEST, were used to train and evaluate the model, respectively. To fairly assess the model performance, comparative analysis between iProDNA-CapsNet and existing state-of-the-art methods was done. Results: Under the decision threshold corresponding to false positive rate (FPR) ≈ 5%, the accuracy, sensitivity, precision, and Matthews's correlation coefficient (MCC) of our model is increased by about 2.0%, 2.0%, 14.0%, and 5.0% with respect to TargetDNA (Hu et al., 2017) and 1.0%, 75.0%, 45.0%, and 77.0% with respect to BindN+ (Wang et al., 2010), respectively. With regards to other methods not reporting their threshold settings, iProDNA-CapsNet also shows a significant improvement in performance based on most of the evaluation metrics. Even with different patterns of change among the models, iProDNA-CapsNets remains to be the best model having top performance in most of the metrics, especially MCC which is boosted from about 8.0% to 220.0%. Conclusions: According to all evaluation metrics under various decision thresholds, iProDNA-CapsNet shows better performance compared to the two current best models (BindN and TargetDNA). Our proposed approach also shows that CapsNet can potentially be used and adopted in other biological applications.

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A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

Cited by 17 publications

References 49 publications

An improved bind-n-seq strategy to determine protein-DNA interactions validated using the bacterial transcriptional regulator YipR

An improved bind-n-seq strategy to determine protein-DNA interactions validated using the bacterial transcriptional regulator YipR

DBpred: A deep learning method for the prediction of DNA interacting residues in protein sequences

IProDNA-CapsNet: Identifying protein-DNA binding residues using capsule neural networks

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